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• UG :- - Not Required

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• No of position :- ( 1 )

• Post :- 14th Mar 2024

Min 13-15+ years exp

Spark/AWS/ Snowflake are main

Need from Hadoop/big data background 

Key skills - Strong SQL/Data warehousing concepts/Pyspark/AWS/ Snowflake

Key Responsibilities -

• Design, implement and support an analytical data infrastructure

• Managing AWS resources including EC2, EMR, S3, Glue, Redshift, etc.

• Interface with other technology teams to extract, transform, and load data from a wide variety of data sources using SQL and AWS big data technologies

• Explore and learn the latest AWS technologies to provide new capabilities and increase efficiency

• Collaborate with Data Scientists and Business Intelligence Engineers (BIEs) to recognize and help adopt best practices in reporting and analysis

• Help continually improve ongoing reporting and analysis processes, automating or simplifying self-service support for customers

• Maintain internal reporting platforms/tools including troubleshooting and development. Interact with internal users to establish and clarify requirements in order to develop report specifications.

• Work with Engineering partners to help shape and implement the development of BI infrastructure including Data Warehousing, reporting and analytics platforms.

• Contribute to the development of the BI tools, skills, culture and impact.

• Write advanced SQL queries and Python code to develop solutions

Skill Sets -

• This position requires a Bachelor's Degree in Computer Science or a related technical field, and relevant employment experience

• Work experience with ETL, Data Modelling, and Data Architecture.

• Expert-level skills in writing and optimizing SQL.

• Experience with Big Data technologies such as Hadoop/Hive/Spark.

• Solid Linux skills.

• Experience operating very large data warehouses or data lakes.

• Expertise in ETL optimization, designing, coding, and tuning big data processes using Apache Spark or similar technologies.

• Experience with building data pipelines and applications to stream and process datasets at low latencies.

• Show efficiency in handling data - tracking data lineage, ensuring data quality, and improving discoverability of data.

• Sound knowledge of distributed systems and data architecture (lambda)- design and implement batch and stream data processing pipelines, knows how to optimize the distribution, partitioning, and MPP of high-level data structures.

• Knowledge of Engineering and Operational Excellence using standard methodologies.